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    "假设你获取了250部电影的时长(列表a中),希望统计出这些电影时长的分布状态(比如时长为100分钟到120分钟电影的数量,出现的频率)等信息,你应该如何呈现这些数据?\n",
    "a=[131,  98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115,  99, 136, 126, 134,  95, 138, 117, 111,78, 132, 124, 113, 150, 110, 117,  86,  95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123,  86, 101,  99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140,  83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137, 116, 103, 144,  83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137,  92,121, 112, 146,  97, 137, 105,  98, 117, 112,  81,  97, 139, 113,134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112,  83,  94, 146, 133, 101,131, 116, 111,  84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#设置中文显示\n",
    "font = {'family':'Microsoft Yahei',\n",
    "        'size':'10'}\n",
    "matplotlib.rc('font',**font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[131,  98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115,  99, 136, 126, 134,  95, 138, 117, 111,78, 132, 124, 113, 150, 110, 117,  86,  95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123,  86, 101,  99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140,  83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137, 116, 103, 144,  83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137,  92,121, 112, 146,  97, 137, 105,  98, 117, 112,  81,  97, 139, 113,134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112,  83,  94, 146, 133, 101,131, 116, 111,  84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#计算组数\n",
    "d = 3  #组距：每个小组两端的距离，根据实际请款选择总组数，一般在20组左右\n",
    "num_bins = (max(a)-min(a))//d #最好能被整除，否则会发生偏移\n",
    "#设置图形的大小\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.hist(a,num_bins)\n",
    "#设置x轴的刻度\n",
    "plt.xticks(range(min(a),max(a)+d,d))#max(a)中若不+d最大值取不到，左闭右开区间\n",
    "#绘制网格\n",
    "plt.grid(alpha=0.5)\n",
    "#设置x,y的label\n",
    "plt.xlabel('电影时长')\n",
    "plt.ylabel('个数')\n",
    "#增加title\n",
    "plt.title('电影时长分布状态图')\n",
    "#展示直方图\n",
    "plt.show()"
   ]
  }
 ]
}